Browsing by Subject "climate"

Now showing 1 - 3 of 3
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    How well can we predict climate migration? A review of forecasting models
    (Frontiers in Climate, 2023-01-01) Schewel, K; Dickerson, S; Madson, B; Nagle Alverio, G
    Climate change will have significant impacts on all aspects of human society, including population movements. In some cases, populations will be displaced by natural disasters and sudden-onset climate events, such as tropical storms. In other cases, climate change will gradually influence the economic, social, and political realities of a place, which will in turn influence how and where people migrate. Planning for the wide spectrum of future climate-related mobility is a key challenge facing development planners and policy makers. This article reviews the state of climate-related migration forecasting models, based on an analysis of thirty recent models. We present the key characteristics, strengths, and weaknesses of different modeling approaches, including gravity, radiation, agent-based, systems dynamics and statistical extrapolation models, and consider five illustrative models in depth. We show why, at this stage of development, forecasting models are not yet able to provide reliable numerical estimates of future climate-related migration. Rather, models are best used as tools to consider a range of possible futures, to explore systems dynamics, to test theories or potential policy effects. We consider the policy and research implications of our findings, including the need for improved migration data collection, enhanced interdisciplinary collaboration, and scenarios-based planning.
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    ItemOpen Access
    Premature Deaths in Africa Due To Particulate Matter Under High and Low Warming Scenarios.
    (GeoHealth, 2022-05) Shindell, D; Faluvegi, G; Parsons, L; Nagamoto, E; Chang, J
    Sustainable development and climate change mitigation can provide enormous public health benefits via improved air quality, especially in polluted areas. We use the latest state-of-the-art composition-climate model simulations to contrast human exposure to fine particulate matter in Africa under a "baseline" scenario with high material consumption, population growth, and warming to that projected under a sustainability scenario with lower consumption, population growth, and warming. Evaluating the mortality impacts of these exposures, we find that under the low warming scenario annual premature deaths due to PM2.5 are reduced by roughly 515,000 by 2050 relative to the high warming scenario (100,000, 175,000, 55,000, 140,000, and 45,000 in Northern, West, Central, East, and Southern Africa, respectively). This reduction rises to ∼800,000 by the 2090s, though by that time much of the difference is attributable to the projected differences in population. By contrast, during the first half of the century benefits are driven predominantly by emissions changes. Depending on the region, we find large intermodel spreads of ∼25%-50% in projected future exposures owing to different physics across the ensemble of 6 global models. The spread of projected deaths attributable to exposure to fine particulate matter, including uncertainty in the exposure-response function, are reduced in every region to ∼20%-35% by the non-linear exposure-response function. Differences between the scenarios have an even narrower spread of ∼5%-25% and are highly statistically significant in all regions for all models. These results provide valuable information for policy-makers to consider when working toward climate change mitigation and sustainable development goals.
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    The Ocean-Land-Atmosphere-Model: Optimization and Evaluation of Simulated Radiative Fluxes and Precipitation
    (2010) Medvigy, David; Walko, Robert L; Otte, Martin J; Avissar, Roni
    This work continues the presentation and evaluation of the Ocean Land Atmosphere Model (OLAM), focusing on the model's ability to represent radiation and precipitation. OLAM is a new, state-of-the-art earth system model, capable of user-specified grid resolution and local mesh refinement. An objective optimization of the microphysics parameterization is carried out. Data products from the Clouds and the Earth's Radiant Energy System (CERES) and the Global Precipitation Climatology Project (GPCP) are used to construct a maximum likelihood function, and thousands of simulations using different values for key parameters are carried out. Shortwave fluxes are found to be highly sensitive to both the density of cloud droplets and the assumed shape of the cloud droplet diameter distribution function. Because there is considerable uncertainty in which values for these parameters to use in climate models, they are targeted as the tunable parameters of the objective optimization procedure, which identified high-likelihood volumes of parameter space as well as parameter uncertainties and covariances. Once optimized, the model closely matches observed large-scale radiative fluxes and precipitation. The impact of model resolution is also tested. At finer characteristic length scales (CLS), smaller-scale features such as the ITCZ are better resolved. It is also found that the Amazon was much better simulated at 100- than 200-km CLS. Furthermore, a simulation using OLAM's variable resolution functionality to cover South America with 100-km CLS and the rest of the world with 200-km CLS generates a precipitation pattern in the Amazon similar to the global 100-km CLS run.